How can we improve the quality of data collected in general practice?

Shemtob, Lara; Beaney, Thomas; Norton, John D.; Majeed, Azeem

doi:10.1136/bmj-2022-071950

Cited by 9 publications

(11 citation statements)

References 27 publications

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“…Nevertheless, data are not collected primarily for research purposes and can have missing or incorrect data. Information on LTCs was based on coded information in the EHR, which may omit some diagnoses given most GP consultations are recorded as free-text, 29 with greater coding rates for conditions which have financial incentives attached to coding. 30 …”

Section: Discussionmentioning

confidence: 99%

Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions

Beaney,

Clarke,

Salman

et al. 2024

Journal of Multimorbidity and Comorbidity

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Background Identifying clusters of co-occurring diseases may help characterise distinct phenotypes of Multiple Long-Term Conditions (MLTC). Understanding the associations of disease clusters with health-related outcomes requires a strategy to assign clusters to people, but it is unclear how the performance of strategies compare. Aims First, to compare the performance of methods of assigning disease clusters to people at explaining mortality, emergency department attendances and hospital admissions over one year. Second, to identify the extent of variation in the associations with each outcome between and within clusters. Methods We conducted a cohort study of primary care electronic health records in England, including adults with MLTC. Seven strategies were tested to assign patients to fifteen disease clusters representing 212 LTCs, identified from our previous work. We tested the performance of each strategy at explaining associations with the three outcomes over 1 year using logistic regression and compared to a strategy using the individual LTCs. Results 6,286,233 patients with MLTC were included. Of the seven strategies tested, a strategy assigning the count of conditions within each cluster performed best at explaining all three outcomes but was inferior to using information on the individual LTCs. There was a larger range of effect sizes for the individual LTCs within the same cluster than there was between the clusters. Conclusion Strategies of assigning clusters of co-occurring diseases to people were less effective at explaining health-related outcomes than a person’s individual diseases. Furthermore, clusters did not represent consistent relationships of the LTCs within them, which might limit their application in clinical research.

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Section: Discussionmentioning

confidence: 99%

Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions

Beaney,

Clarke,

Salman

et al. 2024

Journal of Multimorbidity and Comorbidity

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“…Initially, we aimed to also explore primary care attendance, but coded data on consultation modality in primary care was not available. We have written elsewhere of the urgent need for improvements in coded primary care data (40). Linked datasets such as WSIC offer opportunities for effective service planning, implementation, and evaluation as well as for identifying individuals in need of tailored healthcare services, with the goal of improving health outcomes and healthcare system efficiency.…”

Section: Discussionmentioning

confidence: 99%

Attendance at remote versus face-to-face outpatient appointments in an NHS Trust

Kerr,

Greenfield,

Hayhoe

et al. 2023

Preprint

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IntroductionWith the growing use of remote appointments within the National Health Service, there is a need to understand potential barriers of access to care for some patients. In this observational study we examined missed appointments rates, comparing remote and face-to-face appointments among different patient groups.MethodsWe analysed adult outpatient appointments at Imperial College Healthcare NHS Trust in Northwest London in 2021. Rates of missed appointments per patient were compared between remote vs. face-to-face appointments using negative binomial regression models. Models were stratified by appointment type (first or a follow-up).ResultsThere were 874,659 outpatient appointments for 189,882 patients, 29.5% of whom missed at least one appointment. Missed rates were 12.5% for remote first appointments and 9.2% for face-to-face first appointment. Remote and face-to-face follow-up appointments were missed at similar rates (10.4% and 10.7%, respectively). For remote and face-to-face appointments, younger patients, residents of more deprived areas, and patients of Black, Mixed, and ‘other’ ethnicities missed more appointments. Male patients missed more face-to-face appointments, particularly at younger ages, but gender differences were minimal for remote appointments. Patients with long-term conditions (LTCs) missed more first appointments, whether face-to-face or remote. In follow-up appointments, patients with LTCs missed more face-to-face appointments but fewer remote appointments.DiscussionRemote face-to-face appointments were missed more often than face-to-face first appointments, follow-ups appointments had similar attendance rates for both modalities. Sociodemographic differences in outpatient appointment attendance were largely similar between face-to-face and remote appointments, indicating no widening of inequalities in attendance due to appointment modality.

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“…A better solution is to tackle problems collaboratively and at scale through a learning system approach that includes patients and diverse staff groups. 9 A primary care learning system could use routinely collected data to monitor care, understand problems, identify targets for improvement, co-design and develop prototype solutions, and implement and test changes with a view to improving both patients’ and GPs’ satisfaction.…”

Section: Learning System Approachmentioning

confidence: 99%

Tackling the crisis in general practice

Sinnott¹,

Dorban-Hall

Dixon‐Woods³

2023

BMJ

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How can we improve the quality of data collected in general practice?

Abstract: Lara Shemtob and colleagues argue that clinical staff entering the data need to be more connected to those using the information for research and quality improvement

Cited by 9 publications

References 27 publications

Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions

Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions

Attendance at remote versus face-to-face outpatient appointments in an NHS Trust

Tackling the crisis in general practice

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